Part of the SciDataCon14 workshop on "Data Papers and their applications" run by myself and Brian Hole to help attendees understand current data-publishing journals and trends and help them understand the editorial processes on NPG's Scientific Data and Ubiquity's Open Health Data.

1. SciDataCon2014, 2-5 November, 2014
Data Papers and their applications:!
examples from !
Nature Publishing Group and Ubiquity Press!
1. Introduction!
2. Anatomy of a data paper - cases studies from
specific journals!
• Nature Publishing Group - Scientific Data,
Susanna-Assunta Sansone!
• Ubiquity Press - Open Health Data,
Brian Hole!
3. Feedback and discussion!

2. Consultant,
Honorary Academic Editor
Associate Director,
Principal Investigator
!
Introduction!
The role of publishers and data papers !
!
Susanna-Assunta Sansone, PhD!
!
!
@biosharing!
@isatools!
@scientificdata!
!
SciDataCon2014, 2-5 November, 2014

3. Credit to:
https://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/

4. A community mobilization for “openness”
http://discovery.urlibraries.org/ https://okfn.org
image by Greg Emmerich
• Open data is a means to do better
science more efficiently!
• Licenses, copyright and IP are legal
barriers to data sharing and reuse!
o Licenses are for asserting rights;
waivers are for giving them up,
maximising potential for data reuse,
integration and discovery of new
knowledge!
• Creative Commons CC0!
o interoperability: CC0 is human and
machine-readable!
o universality: CC0 is global and
universal and widely recognized!
o simplicity: no need for humans to
make, and respond to, individual data
requests!
http://opendefinition.org/licenses/
http://pantonprinciples.org
https://www.copyrightsworld.com
https://creativecommons.org

5. Open access is not enough on its own
http://www.theguardian.com/higher-education-network/blog/2014/jun/26
If your research has been funded by
the taxpayer, there's a good chance
you'll be encouraged to publish your
results on an open access basis…..
This final article makes publicly
available the hypotheses,
interpretations and conclusions of your
research.
But what about the data that led you
to those results and conclusions?

6. Also open data is not always enough
http://www.theguardian.com/higher-education-network/blog/2014/jun/26
So data that is in theory open and
free to access!
• may still be hard to get hold of!
• it may not have been stored or cited
in the appropriate manner!
• it may not be interoperable with
related data because it is not
formatted appropriately; or!
• it may not be reusable because it
may not contain enough information
for others to understand it!

7. Movement for FAIR data in life and medical sciences
http://bd2k.nih.gov/workshops.html#ADDS

8. Because, in all fairness, not much data is FAIR!
8

9. Data unavailability and incomplete annotations

10. Benefits and barriers to data sharing
Credit to:
Iain Hrynaszkiewicz
Benefits! Barriers!
• Reduction of error and fraud!
• Increased return on investment in
research!
• Compliance with funder and
journal mandates!
• Reduce duplication and bias!
• Reproduction/validation of
research!
• Testing additional hypotheses!
• Use for teaching!
• Integration with other data sets!
• Increased citations !
• Concerns over inappropriate reuse!
• Limited time/resources!
• Costs associated with data sharing!
• Human privacy concerns!
• Unclear ownership of data/
authority to release data!
• Lack of academic incentives/
recognition!
• Lack of repositories or lack of
awareness of repositories!
• Protecting commercially sensitive
information !

11. Responsibilities lie across several stakeholder groups

12. Role of publishers as “agents of change”
• Data has to become an integral
part of scholarly communications!
!
• Publishers occupy a leverage
point in this process!

13. The role of data journals/articles
• Credit!
• Unpublished data!
• Peer review focus!
• Value of data vs. analysis!
• Discoverability!
• Reusability!
• Narrative/context!
• “Intelligently open data”!
Credit to:
Iain Hrynaszkiewicz

14. Publishers and data/reproducibility
• Policies on access (to data, code, reagents etc.)!
o Supporting funder & community needs!
• Format and amount of content!
o Methodological details, supplementary info, data integration and
links to repositories!
• Licensing for reuse!
• Incentives to share!
o Data citations!
o Data journals and articles!
• Quality assurance through peer review!
Credit to:
Iain Hrynaszkiewicz

15. Nature Publishing Group: the changing landscape
Human Genome 2001
62 Pages, 150 Authors,
49 Figure, 27 tables
Encode Project 2012
30 papers,
3 Journals

16. 2013
Credit to:
Iain Hrynaszkiewicz

17. Data/reproducibility at NPG
Some important recent events 2013-2014
• Figure source data
o putting data behind figures/graphs
o rolled out at Nature and progressively across all other Nature branded
titles
Wang et al, Nature, 2013
doi:10.1038/nature12730

18. Data/reproducibility at NPG
Some important recent events 2013-2014
• Figure source data
o putting data behind figures/graphs
o rolled out at Nature and progressively across all other Nature branded
titles
• Extended data
o expandable text and extra figures; rolled out at Nature

19. Data/reproducibility at NPG
Some important recent events 2013-2014
• Figure source data
o putting data behind figures/graphs
o rolled out at Nature and progressively across all other Nature branded
titles
• Extended data
o expandable text and extra figures; rolled out at Nature
• Data citation
o tackling both styling and format; monitoring community developments,
such the Data Citation Synthesis Group
o to be rolled out across all Nature branded titles and Scientific Data
• Code reproducibility
o peer review, availability and reuse
• NPG’s Linked Data release – CC0
• A new data publication platform:

20. From made reproducible to born reproducible
“Reproducing the method took several months of effort, and
required using new versions and new software that posed
challenges to reconstructing and validating the results”

21. Data journals everywhere?
Credit to:
Iain Hrynaszkiewicz

22. Consultant,
Honorary Academic Editor
Associate Director,
Principal Investigator
!
!
!
!
!
!
!
!
!
!
@scientificdata!
Susanna-Assunta Sansone, PhD!
@biosharing!
@isatools!
!
!
SciDataCon2014, 2-5 November, 2014
A new open-access, online-only publication for
descriptions of scientifically valuable datasets !

23. • Get Credit for Sharing Your Data
• Publications will be listed in the major indexes and will be citeable
• Focused on Data Reuse
• All the information others need to reuse the data; no interpretative
analysis or hypothesis testing
• Open-access
• Authors select from three Creative Commons licences for the main
• Data Descriptor. Each publication supported by curated CC0
metadata
• Peer-reviewed
• Rigorous peer-review managed by our Editorial Board of academic
researchers ensures data quality and standards
• Promoting Community Data Repositories
• Data stored in community data repositories

24. Introducing a new content type: the Data Descriptor
• Designed to make data more discoverable, interpretable and
reusable!
• Concerned with the facts behind the methodology
of data generation/collection and processing!
• Complements a journal article!
Synthesis
Analysis
Data Descriptor
Conclusions
Interpretation
What is the
sample?
What did I do to
generate the data?
How was the data
processed?
Where is the data?
Who did what when?
Summary of
Data
Descriptor
Facts
Data Descriptor
Journal article
NARRATIVE

25. Data Descriptor: narrative and structure!
!
!
!
Experimental metadata or !
structured component!
(in-house curated, machine-readable
formats)!
Article or !
narrative component!
(PDF and HTML) !

26. Data Descriptor: narrative!
Focus on data reuse!
Detailed descriptions of the methods and technical analyses supporting the
quality of the measurements.!
Does not contain tests of new scientific hypotheses!
In traditional publications this
information is not provided in a
sufficiently detailed manner
However this information is
essential for understanding,
reusing, and reproducing
datasets
Sections:!
• Title!
• Abstract!
• Background & Summary!
• Methods!
• Technical Validation!
• Data Records!
• Usage Notes !
• Figures & Tables !
• References!
• Data Citations!
!

27. Data Descriptor: narrative!
Focus on data reuse!
Detailed descriptions of the methods and technical analyses supporting the
quality of the measurements.!
Does not contain tests of new scientific hypotheses!
Sections:!
• Title!
• Abstract!
• Background & Summary!
• Methods!
• Technical Validation!
• Data Records!
• Usage Notes !
• Figures & Tables !
• References!
• Data Citations!
!

28. Data Descriptor: narrative!
Focus on data reuse!
Detailed descriptions of the methods and technical analyses supporting the
quality of the measurements.!
Does not contain tests of new scientific hypotheses!
Sections:!
• Title!
• Abstract!
• Background & Summary!
• Methods!
• Technical Validation!
• Data Records!
• Usage Notes !
• Figures & Tables !
• References!
• Data Citations!
!
Joint Declaration of Data Citation Principles by the
Data Citation Synthesis Group

29. Data Descriptor: structure - content !
General-purpose, machine-readable
format, designed to
support:
• description of the experimental
workflow
• explicit and discoverable
annotations
• provenance tracking
• use community-defined
minimal reporting guidelines
and terminologies
Data file or !
record in a
database!
analysis !
method! script!

30. Data Descriptor: structure - content !
Includes fields describing:
• each study, linking to relevant
sections of the Data Descriptor
article
• authors’ details, including ORCID
• publications
• funding sources and funders’ name,
via FundRef
• experimental factors
• study design
• assays
• protocols
Data file or !
record in a
database!
analysis !
method! script!

31. Data Descriptor: structure - content !
It allows to relate samples, and
their descriptions to the data files

32. Data Descriptor: structure - content !
In-house editorial curator:!
• assists users to submit the structured
content via simple templates and an
internal authoring tool!
• performs value-added semantic
annotation of the experimental
metadata!
For advanced users/service providers
willing to export ISA-Tab for direct
submission, we have released a technical
specification:!
Data file or !
record in a
database!
analysis !
method! script!

33. Workflow overview!
Green: author; Purple: repository; Blue: SciData; Red: production

34. Collect
Data!
Publish your data early!
Follow-up
experiments!
Publish
Findings!
Publish
Data!
Scientific Data’s prior publication policy with other NPG journals
protects your ability to publish the screen data and the hits later
Credit to:
Andrew Hufton

35. Hao et al.: Environmental!
8 citations
Data sets from the Global Integrated
Drought Monitoring and Prediction
System (GIDMaPS), which provides
drought information based on multiple
drought indicators

36. Hao et al.: Environmental!
8 citations
New Dataset
• Data in figshare
• Code in figshare

37. Hao et al.: Environmental!
8 citations
New Dataset
• Data in figshare
• Code in figshare
• Cited in Science

38. Or your data and findings simultaneously/after!
Collect
Data!
Follow-up
experiments!
Publish
Findings!
Submit
Data!
Hold
publication!
Scientific Data will hold a Data Descriptor publication that has
been accepted for publication, while your other related research
publications clear peer review
Credit to:
Andrew Hufton

39. Messina et al.: Epidemiology!
4 citations
The most comprehensive geographic
collection of human dengue virus
occurrence data (1960 -2012), linked
to point or polygon locations, derived
from peer-reviewed literature and
case reports as well as informal online
sources

40. !
!
!
!
!
!
!
!
Scientific hypotheses:!
Synthesis!
Analysis!
Conclusions!
Messina et al.: Epidemiology! 4 citations
Associated Nature Article
• Data in figshare
Methods and technical analyses supporting
the quality of the measurements:!
What did I do to generate the data?!
How was the data processed?!
Where is the data?!
Who did what when!

41. Value added component integrated in a
growing ecosystem!
Research
papers
Descriptors
Data
Data
records

42. Progressively refine the guidance to authors !
Over 500 Over 600
A web-based, curated and searchable portal works to ensure the
standards and databases are registered, informative and discoverable
and accessible, monitoring the development and evolution of standards,
their use in databases and the adoption of both in data policies.

43. Helping authors find the right place for the data!
• We currently recognize over 60 public data
repositories, and provide advice on the best
place for authors to archive their data!
• We have integrated systems with both:!
!
!
2
4
3
10 4
1
4
3
4
“Omics” is emphasized
among basic life-sciences
repositories
DNA and protein sequence
Functional genomics
Genetic association and genome variation
Metagenomics
Molecular interactions
Organism- or disease-specific
Proteomics
Taxonomy and species diversity
Traces and sequencing reads

44. 4 Big
data
|
CSE
2014
4
Repositories criteria!
1. Broad support and recognition within their scientific community !
2. Ensure long-term persistence and preservation of datasets!
3. Provide expert curation !
4. Implement relevant, community-endorsed reporting requirements !
Progressively monitor this via !
5. Provide for confidential review of submitted datasets !
6. Provide stable identifiers for submitted datasets !
7. Allow public access to data without unnecessary restrictions !

45. Citations of and links to data files - databases!

46. Peer review process focused on quality and reuse!
Evaluation is not be based on the perceived impact !
or novelty of the findings or size of the data!
!
• Experimental rigour and technical data quality!
o Methodologically sound!
o Technical validation experiments and statistical analyses!
o Depth, coverage, size, and/or completeness of data sufficient for the types
of applications!
• Completeness of the description!
o Sufficient details to allow others to reproduce the results, reuse or
integrate it with other data!
o Compliance with relevant minimum information or reporting standards!
• Integrity of the data files and repository record!
o Data files match the descriptions in the Data Descriptor!
o Deposited in the most appropriate available data repository!

47. Current content is diverse - bimonthly releases !
• Neuroscience, ecology, epidemiology, environmental science, functional
genomics, metabolomics, toxicology etc.!
• New previously published individual datasets, curated aggregation and
citizen science:!
o a fuller, more in-depth look at the data processing steps, supported by
additional data files and code from each step!
o additional tutorial-like information for scientists interested in reusing or
integrating the data with their own!
• Datasets in figshare, Dryad and domain specific databases!
• Code deposited in figshare and GitHub!
• First collection:!
47

49. Open Access – APC supported!
Data: the primary datasets resides in public
repositories. Partnering with FigShare and Dryad,
which are both CC0!
Data Descriptor - structured component (ISA-Tab):
as NPG has already done with its existing Linked
Data Portal, the metadata about data descriptors in
Scientific Data is CC0!
Data Descriptor - narrative component: describing
the methodology of data generation/collection and
processing is licensed under either of the following, by
author choice:
OA Article processing charges: $1,000 USD / £650 GBP / €750 for each accepted article

50. Supported by:!
Advisory Panel including senior researchers, funders, librarians and curators
Michael Huerta ● National Institutes of Health, USA ● Mark Thorley ● Natural Environment Research
Council, UK ● Patricia Cruse ● University of California, USA ● Susan Gregurick ● Office of
Biological and Environmental Research, Department of Energy, USA ● Ioannis Xenarios ● Swiss
Institute of Bioinformatics, Switzerland ● Chris Bowler ● IBENS, France ● Mark Forster ● Syngenta,
UK ● Anthony Rowe ● Johnson & Johnson, USA ● Stephen Chanock ● National Cancer Institute,
USA ● Weida Tong ● National Center for Toxicological Research, FDA, USA ● Albert J. R. Heck ●
Utrecht University, The Netherlands ● Johanna McEntyre ● EMBL-EBI, European Bioinformatics
Institute, UK ● Simon Hodson ● CODATA, France ● Joseph R. Ecker ● Howard Hughes Medical
Institute & Salk Institute, USA ● Stephen Friend ● Sage Bionetworks, USA ● Jessica Tenenbaum ●
Duke Translational Medicine Institute, USA ● Anne-Claude Gavin ● EMBL, Germany ● David Carr ●
Wellcome Trust, UK ● Wolfram Horstmann ● Göttingen State and University Library, Germany ●
Piero Carninci ● RIKEN Omics Science Center, Japan ● Pascale Gaudet ● Swiss Institute of
Bioinformatics, Switzerland ● Judith A. Blake ● The Jackson Laboratory, USA ● Richard H.
Scheuermann ● J. Craig Venter Institute, USA ● Caroline Shamu ● Harvard Medical School, USA
Susanna-Assunta Sansone
Honorary Academic Editor
(University of Oxford, UK)
Andrew L Hufton
Managing Editor
Varsha Khodiyar
Editorial Curator
Iain Hrynaszkiewicz
Publisher
An open access, peer-reviewed publication for
descriptions of scientifically valuable datasets!
Launched May 2014

51. SciDataCon2014, 2-5 November, 2014
Data Papers and their applications:!
examples from !
Nature Publishing Group and Ubiquity Press!
Feedback and discussion!
• Based on what you have heard today, how well do
these journals fit with your/researchers at your
instituteʼs publication and data management workflow? !
• What are the benefits to data publication? !
• What are the risks/barriers?!
• What can publishers/journal do to incentivise data
publication?!